A Neural Data Security Model: Ensure high confidentiality and security in cloud datastorage environment

摘要

Cloud computing is a computing paradigm which provides a dynamic environment for end users to guarantee Quality of Service (QoS) on data towards confidentiality on the out sourced data. Confidentiality is about accessing a set of information from a cloud database with a high security level. This research proposes a new cloud data security model, I Neurol Data Security Model to ensure high confidentiality and security in cloud data storage environment for achieving data confidentiality in the cloud database platform. This cloud Neurol Data Security Model comprises Dynamic Hashing Fragmented Component and Feedback Neurol Data Security Component. The data security component deals with data encryption for sensitive data using the RSA algorithm to increase the confidentiality level. The fragmented sensitive data is stored in dynamic hashing. The Feedback Neurol Data Security Component is used to encrypt and decrypt the sensitive data by using Feedback Neural Network, This Feedback Neural Network is deployed using the RS. I security algorithm. This work is efficient and effective for all kinds of queries requested by the user. The performance of this work is better than the conventional cloud data security models as it achieve a high data confidentiality level.